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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/video_notebooks/pytorch_2_intro_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PyTorch 2 Quick Intro\n",
        "\n",
        "* Reference notebook - https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/pytorch_2_intro.ipynb \n",
        "* Reference book chapter - https://www.learnpytorch.io/pytorch_2_intro/ \n",
        "* PyTorch 2.0 release notes - https://pytorch.org/blog/pytorch-2.0-release/ "
      ],
      "metadata": {
        "id": "-ZpcEPl0GHmJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "print(torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBJBJFVGGHdu",
        "outputId": "bec241b0-3b28-4c63-d9d7-15fa6f1131cd"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.0+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wav97ye-FgxL",
        "outputId": "e606443c-ffca-468c-f0eb-34944f7d8892"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Apr 27 04:12:43 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    47W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Quick code examples"
      ],
      "metadata": {
        "id": "p7rIodpgG7nH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Before PyTorch 2.0"
      ],
      "metadata": {
        "id": "LUhjfMWpH5Ai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "model = torchvision.models.resnet50()"
      ],
      "metadata": {
        "id": "ayIooWPdH6h6"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### After PyTorch 2.0"
      ],
      "metadata": {
        "id": "XWdHkVY-IAFW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: some PyTorch 2.0 features may hinder the deployment of models - https://pytorch.org/get-started/pytorch-2.0/#inference-and-export "
      ],
      "metadata": {
        "id": "RRwKmr5dIXvj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "model = torchvision.models.resnet50() # note: this could any model\n",
        "compiled_model = torch.compile(model)\n",
        "\n",
        "### Training code\n",
        "\n",
        "### Testing code\n"
      ],
      "metadata": {
        "id": "s9CdCaMHIC35"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Getting setup "
      ],
      "metadata": {
        "id": "E06sXzTuIMye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Check PyTorch version\n",
        "pt_version = torch.__version__\n",
        "print(f\"[INFO] Current PyTorch version: {pt_version} (should be 2.x+)\")\n",
        "\n",
        "# Install PyTorch 2.0 if necessary\n",
        "if pt_version.split(\".\")[0] == \"1\": # Check if PyTorch version begins with 1 \n",
        "    !pip3 install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
        "    print(\"[INFO] PyTorch 2.x installed, if you're on Google Colab, you may need to restart your runtime.\\\n",
        "          Though as of April 2023, Google Colab comes with PyTorch 2.0 pre-installed.\")\n",
        "    import torch\n",
        "    pt_version = torch.__version__\n",
        "    print(f\"[INFO] Current PyTorch version: {pt_version} (should be 2.x+)\")\n",
        "else:\n",
        "    print(\"[INFO] PyTorch 2.x installed, you'll be able to use the new features.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QY2iRZII6R9",
        "outputId": "88fe1132-0c0c-4957-d613-4ded83655dd0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Current PyTorch version: 2.0.0+cu118 (should be 2.x+)\n",
            "[INFO] PyTorch 2.x installed, you'll be able to use the new features.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Get GPU info\n",
        "\n",
        "Why get GPU info?\n",
        "\n",
        "Because PyTorch 2.0 features (torch.compile()) work best on newer NVIDIA GPUs.\n",
        "\n",
        "Well, what's a newer NVIDIA GPU?\n",
        "\n",
        "To find out if your GPU is compatiable, see NVIDIA GPU compability scores - https://developer.nvidia.com/cuda-gpus \n",
        "\n",
        "If your GPU has a score of 8.0+, it can leverage *most* if not *all* of the new PyTorch 2.0 features.\n",
        "\n",
        "GPUs under 8.0 can still leverage PyTorch 2.0, however, the improvements may not be as noticable as those with 8.0+. \n",
        "\n",
        "**Note:** If you're wondering what GPU you should use for deep learning, check out Tim Dettmers blog post \"Which GPU for deep learning?\" - https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/ "
      ],
      "metadata": {
        "id": "slxdwwyLJEW2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make sure we're using a NVIDIA GPU\n",
        "if torch.cuda.is_available():\n",
        "  gpu_info = !nvidia-smi\n",
        "  gpu_info = '\\n'.join(gpu_info)\n",
        "  if gpu_info.find(\"failed\") >= 0:\n",
        "    print(\"Not connected to a GPU, to leverage the best of PyTorch 2.0, you should connect to a GPU.\")\n",
        "\n",
        "  # Get GPU name\n",
        "  gpu_name = !nvidia-smi --query-gpu=gpu_name --format=csv\n",
        "  gpu_name = gpu_name[1]\n",
        "  GPU_NAME = gpu_name.replace(\" \", \"_\") # remove underscores for easier saving\n",
        "  print(f'GPU name: {GPU_NAME}')\n",
        "\n",
        "  # Get GPU capability score\n",
        "  GPU_SCORE = torch.cuda.get_device_capability()\n",
        "  print(f\"GPU capability score: {GPU_SCORE}\")\n",
        "  if GPU_SCORE >= (8, 0):\n",
        "    print(f\"GPU score higher than or equal to (8, 0), PyTorch 2.x speedup features available.\")\n",
        "  else:\n",
        "    print(f\"GPU score lower than (8, 0), PyTorch 2.x speedup features will be limited (PyTorch 2.x speedups happen most on newer GPUs).\")\n",
        "  \n",
        "  # Print GPU info\n",
        "  print(f\"GPU information:\\n{gpu_info}\")\n",
        "\n",
        "else:\n",
        "  print(\"PyTorch couldn't find a GPU, to leverage the best of PyTorch 2.0, you should connect to a GPU.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYrYVATfJHW9",
        "outputId": "a626dd3b-d7cd-4d7b-b499-10142855e236"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU name: NVIDIA_A100-SXM4-40GB\n",
            "GPU capability score: (8, 0)\n",
            "GPU score higher than or equal to (8, 0), PyTorch 2.x speedup features available.\n",
            "GPU information:\n",
            "Thu Apr 27 04:13:01 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    47W / 400W |      3MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.1 Globally set devices\n",
        "\n",
        "Previously, we've set the device of our tensors/models using `.to(device)`.\n",
        "\n",
        "* `tensor.to(device)`\n",
        "* `model.to(device)`\n",
        "\n",
        "But in PyTorch 2.0, it's possible to set the device with a context manager as well as a global device - https://pytorch.org/blog/pytorch-2.0-release/#beta-torchset_default_device-and-torchdevice-as-context-manager \n",
        "\n",
        "See the docs - https://pytorch.org/tutorials/recipes/recipes/changing_default_device.html "
      ],
      "metadata": {
        "id": "FviitA5lKvRP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Set the device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" \n",
        "\n",
        "# Set the device with context manager (requires PyTorch 2.x+)\n",
        "with torch.device(device):\n",
        "  # All tensors or PyTorch objects created in the context manager will be on the target device without using .to()\n",
        "  layer = torch.nn.Linear(20, 30)\n",
        "  print(f\"Layer weights are on device: {layer.weight.device}\")\n",
        "  print(f\"Layer creating data on device: {layer(torch.randn(128, 20)).device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CyDc_zmLyY9",
        "outputId": "0a2b869d-85e6-481d-88be-9ed953ea9308"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer weights are on device: cuda:0\n",
            "Layer creating data on device: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Set the device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# Set the device globally (requires pytorch 2.x+)\n",
        "torch.set_default_device(device) \n",
        "\n",
        "# All tensors or PyTorch objects created from here on out will be on the target device without using .to()\n",
        "layer = torch.nn.Linear(20, 30)\n",
        "print(f\"Layer weights are on device: {layer.weight.device}\")\n",
        "print(f\"Layer creating data on device: {layer(torch.randn(128, 20)).device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szDRJN3uMwpk",
        "outputId": "290acaac-45b5-44fb-98db-6f19f8e5a3fd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer weights are on device: cuda:0\n",
            "Layer creating data on device: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Set the device globally (requires pytorch 2.x+)\n",
        "torch.set_default_device(\"cpu\") \n",
        "\n",
        "# All tensors or PyTorch objects created from here on out will be on the target device without using .to()\n",
        "layer = torch.nn.Linear(20, 30)\n",
        "print(f\"Layer weights are on device: {layer.weight.device}\")\n",
        "print(f\"Layer creating data on device: {layer(torch.randn(128, 20)).device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59I-7C3dNtxh",
        "outputId": "701fb372-9698-4a9a-fb05-76c4ce19be14"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer weights are on device: cpu\n",
            "Layer creating data on device: cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Setting up the experiments \n",
        "\n",
        "Time to test speed!\n",
        "\n",
        "To keep things simple, we'll run 4 experiments:\n",
        "\n",
        "* Model: ResNet50 from torchvision\n",
        "* Data: CIFAR10 from torchvision\n",
        "* Epochs: 5 (single run) and 3x5 (multi run)\n",
        "* Batch size: 128 (note: you may want to change this depending on the amount of memory your GPU has, this tutorial focuses on an A100) \n",
        "* Image size: 224 (note: you may want to adjust this given the amount of GPU memory you have)"
      ],
      "metadata": {
        "id": "n1mweOtnN4hs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "print(f\"PyTorch version: {torch.__version__}\")\n",
        "print(f\"TorchVision version: {torchvision.__version__}\")\n",
        "\n",
        "# Set the target device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "print(f\"Using device: {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPn0zK1xOoxT",
        "outputId": "a94bef4f-1d06-4633-9eac-00cc98b906db"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch version: 2.0.0+cu118\n",
            "TorchVision version: 0.15.1+cu118\n",
            "Using device: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Create model and transforms\n",
        "\n",
        "* ResNet50 from PyTorch - https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html#torchvision.models.ResNet50_Weights"
      ],
      "metadata": {
        "id": "G3Y04VqEPvtg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model weights and transforms\n",
        "model_weights = torchvision.models.ResNet50_Weights.IMAGENET1K_V2 # .DEFAULT also works here\n",
        "transforms = model_weights.transforms()\n",
        "\n",
        "transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uGRvjJBQnsR",
        "outputId": "3855b40e-0651-40d0-d058-3953ff4cd979"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageClassification(\n",
              "    crop_size=[224]\n",
              "    resize_size=[232]\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BILINEAR\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model\n",
        "model = torchvision.models.resnet50(weights=model_weights)\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sbR6zlERDOX",
        "outputId": "f1a0dbad-6498-4d0d-ffc2-6a08ba6fa9ca"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-11ad3fa6.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth\n",
            "100%|██████████| 97.8M/97.8M [00:01<00:00, 93.0MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of parameters in the model\n",
        "total_params = sum(\n",
        "    param.numel() for param in model.parameters() # count all parameters\n",
        "    # param.numel() for param in model.parameters() if param.requires_grad = True # only count parameters that are trainable\n",
        ")\n",
        "total_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ac1B-pJARIfh",
        "outputId": "581a739d-5554-4f53-b117-a783786c5241"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25557032"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** PyTorch 2.0 *relative* speedups will be most noticeable when as much of the GPU as possible is being used. This means a larger model (more trainable parameters) may take longer to train on the whole but will relatively faster. E.g. a model with 1M parameters may take ~10 minutes to train but a model with 25M parameters may take ~20 minutes to train. "
      ],
      "metadata": {
        "id": "gnpsuSObRmrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(num_classes=10):\n",
        "  \"\"\"\n",
        "  Creates a resnet50 model with transforms and returns them both.\n",
        "  \"\"\" \n",
        "  model_weights = torchvision.models.ResNet50_Weights.DEFAULT\n",
        "  transforms = model_weights.transforms()\n",
        "  model = torchvision.models.resnet50(weights=model_weights)\n",
        "\n",
        "  # Adjust the head layer to suit our number of classes\n",
        "  model.fc = torch.nn.Linear(in_features=2048,\n",
        "                             out_features=num_classes)\n",
        "\n",
        "  return model, transforms\n",
        "\n",
        "model, transforms = create_model()\n",
        "transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a87rLsyfRihS",
        "outputId": "c889e8c9-84e3-4c85-c4e3-70029d93357a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageClassification(\n",
              "    crop_size=[224]\n",
              "    resize_size=[232]\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BILINEAR\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2 Speedups are most noticeable when a large portion of the GPU(s) is being used\n",
        "\n",
        "Since modern GPUs are so *fast* at performing operations, you will often notice the majority of *relative* speedups when as much data as possible is on the GPU.\n",
        "\n",
        "In practice, you generally want to use as much of your GPU memory as possible.\n",
        "\n",
        "* Increasing the batch size - we've been using batch size 32 but for GPUs with a larger memory capacity you'll generally want to use as large as possible, e.g. 128, 256, 512, etc\n",
        "* Increasing data size - for example instead of using images that are 32x32, use 224x224 or 336x336, also you could use an increased embedding size for your data \n",
        "* Increase the model size - for example instead of using a model with 1M parameters, use a model with 10M parameters\n",
        "* Decreasing data transfer - since bandwidth costs (transferring data) will slow down a GPU (because it wants to compute on data) \n",
        "\n",
        "As a result of doing the above, your relative speedups should be better.\n",
        "\n",
        "E.g. overall training time may take longer but not linearly. \n",
        "\n",
        "Resource for learning how to improve PyTorch model speed - https://sebastianraschka.com/blog/2023/pytorch-faster.html \n",
        "\n",
        "**Note:** This concept of using as much data on the GPU as possible isn't restricted specificially to PyTorch 2.0, it applies to all versions on PyTorch and basically all models that train on GPUs."
      ],
      "metadata": {
        "id": "JXw942TgeNNJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.3 Checking the memory limits of our GPU\n",
        "\n",
        "Can do so using `torch.cuda` - https://pytorch.org/docs/stable/generated/torch.cuda.mem_get_info.html#torch.cuda.mem_get_info "
      ],
      "metadata": {
        "id": "OyCgvQlehke1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check available GPU memory and total GPU memory\n",
        "total_free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() \n",
        "print(f\"Total free GPU memory: {round(total_free_gpu_memory * 1e-9, 3)} GB\") \n",
        "print(f\"Total GPU memory: {round(total_gpu_memory * 1e-9, 3)} GB\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Pe4EBMihsVi",
        "outputId": "e3d59271-8d2b-4bc4-ba7a-88d91cb0d987"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total free GPU memory: 41.048 GB\n",
            "Total GPU memory: 42.482 GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* If the GPU has 16GB+ of free memory, set batch size to 128\n",
        "* If the GPU has less than 16GB of free, set batch size to 32"
      ],
      "metadata": {
        "id": "wWqlWXHaisg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set batch size depending on amount of GPU memory\n",
        "total_free_gpu_memory_gb = round(total_free_gpu_memory * 1e-9, 3)\n",
        "if total_free_gpu_memory_gb >= 16:\n",
        "  BATCH_SIZE = 128 # Note: you could experiment with higher values here if you like.\n",
        "  IMAGE_SIZE = 224\n",
        "  print(f\"GPU memory available is {total_free_gpu_memory_gb} GB, using batch size of {BATCH_SIZE} and image size {IMAGE_SIZE}\")\n",
        "else:\n",
        "  BATCH_SIZE = 32\n",
        "  IMAGE_SIZE = 128\n",
        "  print(f\"GPU memory available is {total_free_gpu_memory_gb} GB, using batch size of {BATCH_SIZE} and image size {IMAGE_SIZE}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NijMb4zEhs4u",
        "outputId": "2b2b987a-516f-462f-813f-c3e6a64d6385"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU memory available is 41.048 GB, using batch size of 128 and image size 224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transforms.crop_size = 224\n",
        "transforms.resize_size = 224 \n",
        "print(f\"Updated data transforms:\\n{transforms}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqsiNlhNjKSW",
        "outputId": "4fd5551a-3f3a-4d61-bc56-a378b9b9a8e3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data transforms:\n",
            "ImageClassification(\n",
            "    crop_size=224\n",
            "    resize_size=224\n",
            "    mean=[0.485, 0.456, 0.406]\n",
            "    std=[0.229, 0.224, 0.225]\n",
            "    interpolation=InterpolationMode.BILINEAR\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4 More potential speedups with TF32\n",
        "\n",
        "TF32 = TensorFloat32\n",
        "\n",
        "TesnorFloat32 = a datatype that bridges Float32 and Float16\n",
        "\n",
        "Float32 = a number is represented by 32 bytes (e.g. 010101010110011 = 7)\n",
        "\n",
        "Float16 = a number is represented by 16 bytes (e.g. 01010101 = 4)\n",
        "\n",
        "See more on precision in computing: https://en.wikipedia.org/wiki/Precision_(computer_science) \n",
        "\n",
        "What we want is:\n",
        "1. Fast model training \n",
        "2. Accurate model training\n",
        "\n",
        "TensorFloat32 = a datatype from NVIDIA which combines float32 and float16\n",
        "\n",
        "TF32 is available on Ampere GPUs+ "
      ],
      "metadata": {
        "id": "kB9_TPnTjNXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "GPU_SCORE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YMfI06q38HD",
        "outputId": "9ff31aa7-89de-421f-a1ef-98601fca2bb0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if GPU_SCORE >= (8, 0): # check if GPU is compatiable with TF32\n",
        "  print(f\"[INFO] Using GPU with score: {GPU_SCORE}, enabling TensorFloat32\")\n",
        "  torch.backends.cuda.matmul.allow_tf32 = True\n",
        "else:\n",
        "  print(f\"[INFO] Using GPU with score: {GPU_SCORE}, TensorFloat32 not available\") \n",
        "  torch.backends.cuda.matmul.allow_tf32 = False"
      ],
      "metadata": {
        "id": "pSRX1uXFjpX-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a09907c7-da86-408f-e7fb-e92fe697b3f5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Using GPU with score: (8, 0), enabling TensorFloat32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.5 Preparing datasets\n",
        "\n",
        "As before, we discussed we're going to use CIFAR10.\n",
        "\n",
        "Homepage - https://www.cs.toronto.edu/~kriz/cifar.html \n",
        "\n",
        "We can download the dataset from torchvision - https://pytorch.org/vision/main/generated/torchvision.datasets.CIFAR10.html "
      ],
      "metadata": {
        "id": "KIw_BKIE3RKD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jh_mS8xR5hgo",
        "outputId": "0a98cbe5-abe9-4e73-cb2d-5f8b2eba0324"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageClassification(\n",
              "    crop_size=224\n",
              "    resize_size=224\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BILINEAR\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create train and test datasets\n",
        "import torchvision\n",
        "train_dataset = torchvision.datasets.CIFAR10(root=\".\", # where to store data\n",
        "                                             train=True, # do we want training dataset?\n",
        "                                             download=True,\n",
        "                                             transform=transforms\n",
        "                                             )\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root=\".\",\n",
        "                                            train=False,\n",
        "                                            download=True,\n",
        "                                            transform=transforms)\n",
        "\n",
        "# Get the length of the datasets\n",
        "train_len = len(train_dataset)\n",
        "test_len = len(test_dataset)\n",
        "\n",
        "print(f\"[INFO] Train dataset length: {train_len}\")\n",
        "print(f\"[INFO] Test dataset length: {test_len}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMW-oU405IGv",
        "outputId": "848e71b4-1c7f-49cc-eb58-f8670d492f58"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:13<00:00, 12868066.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to .\n",
            "Files already downloaded and verified\n",
            "[INFO] Train dataset length: 50000\n",
            "[INFO] Test dataset length: 10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[0][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZ4-3BtT5-g9",
        "outputId": "7762bd0b-07d4-452e-f9ab-acf5101b5cf2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.6 Create DataLoaders\n",
        "\n",
        "Next:\n",
        "* Turn datasets into DataLoaders"
      ],
      "metadata": {
        "id": "ekt-FqlE6H9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE * 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32ZjKr8j7mk9",
        "outputId": "b80046ad-728d-43b1-a263-f107e2b933ba"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "256"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import os\n",
        "NUM_WORKERS = os.cpu_count() # we want highest number of CPU cores to load data to GPU \n",
        "\n",
        "train_dataloader = DataLoader(dataset=train_dataset,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True,\n",
        "                              num_workers=NUM_WORKERS)\n",
        "\n",
        "test_dataloader = DataLoader(dataset=test_dataset,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False,\n",
        "                             num_workers=NUM_WORKERS)\n",
        "\n",
        "# Print details:\n",
        "print(f\"Train dataloader num batches: {len(train_dataloader)} of batch size: {BATCH_SIZE}\")\n",
        "print(f\"Test dataloader num batches: {len(test_dataloader)} of batch size: {BATCH_SIZE}\")\n",
        "print(f\"Using num workers to load data (more is generally better): {NUM_WORKERS}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcO2r5mI6V-z",
        "outputId": "cd70f058-8a90-4a06-cd52-57d439e636c2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataloader num batches: 391 of batch size: 128\n",
            "Test dataloader num batches: 79 of batch size: 128\n",
            "Using num workers to load data (more is generally better): 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.7 Creating training and test loops \n",
        "\n",
        "Want to create: \n",
        "* Training and test loops + a timing step for each, so we know how long our models take to train/test\n",
        "\n",
        "We covered this functionality in previous sections, one is here: https://www.learnpytorch.io/05_pytorch_going_modular/#4-creating-train_step-and-test_step-functions-and-train-to-combine-them"
      ],
      "metadata": {
        "id": "3Z0MQUwD7Hn5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "from tqdm.auto import tqdm\n",
        "from typing import Dict, List, Tuple\n",
        "\n",
        "def train_step(epoch: int,\n",
        "               model: torch.nn.Module, \n",
        "               dataloader: torch.utils.data.DataLoader, \n",
        "               loss_fn: torch.nn.Module, \n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               device: torch.device,\n",
        "               disable_progress_bar: bool = False) -> Tuple[float, float]:\n",
        "  \"\"\"Trains a PyTorch model for a single epoch.\n",
        "\n",
        "  Turns a target PyTorch model to training mode and then\n",
        "  runs through all of the required training steps (forward\n",
        "  pass, loss calculation, optimizer step).\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be trained.\n",
        "    dataloader: A DataLoader instance for the model to be trained on.\n",
        "    loss_fn: A PyTorch loss function to minimize.\n",
        "    optimizer: A PyTorch optimizer to help minimize the loss function.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A tuple of training loss and training accuracy metrics.\n",
        "    In the form (train_loss, train_accuracy). For example:\n",
        "\n",
        "    (0.1112, 0.8743)\n",
        "  \"\"\"\n",
        "  # Put model in train mode\n",
        "  model.train()\n",
        "\n",
        "  # Setup train loss and train accuracy values\n",
        "  train_loss, train_acc = 0, 0\n",
        "\n",
        "  # Loop through data loader data batches\n",
        "  progress_bar = tqdm(\n",
        "        enumerate(dataloader), \n",
        "        desc=f\"Training Epoch {epoch}\", \n",
        "        total=len(dataloader),\n",
        "        disable=disable_progress_bar\n",
        "    )\n",
        "\n",
        "  for batch, (X, y) in progress_bar:\n",
        "      # Send data to target device\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      # 1. Forward pass\n",
        "      y_pred = model(X)\n",
        "\n",
        "      # 2. Calculate  and accumulate loss\n",
        "      loss = loss_fn(y_pred, y)\n",
        "      train_loss += loss.item() \n",
        "\n",
        "      # 3. Optimizer zero grad\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # 4. Loss backward\n",
        "      loss.backward()\n",
        "\n",
        "      # 5. Optimizer step\n",
        "      optimizer.step()\n",
        "\n",
        "      # Calculate and accumulate accuracy metric across all batches\n",
        "      y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)\n",
        "      train_acc += (y_pred_class == y).sum().item()/len(y_pred)\n",
        "\n",
        "      # Update progress bar\n",
        "      progress_bar.set_postfix(\n",
        "            {\n",
        "                \"train_loss\": train_loss / (batch + 1),\n",
        "                \"train_acc\": train_acc / (batch + 1),\n",
        "            }\n",
        "        )\n",
        "\n",
        "\n",
        "  # Adjust metrics to get average loss and accuracy per batch \n",
        "  train_loss = train_loss / len(dataloader)\n",
        "  train_acc = train_acc / len(dataloader)\n",
        "  return train_loss, train_acc\n",
        "\n",
        "def test_step(epoch: int,\n",
        "              model: torch.nn.Module, \n",
        "              dataloader: torch.utils.data.DataLoader, \n",
        "              loss_fn: torch.nn.Module,\n",
        "              device: torch.device,\n",
        "              disable_progress_bar: bool = False) -> Tuple[float, float]:\n",
        "  \"\"\"Tests a PyTorch model for a single epoch.\n",
        "\n",
        "  Turns a target PyTorch model to \"eval\" mode and then performs\n",
        "  a forward pass on a testing dataset.\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be tested.\n",
        "    dataloader: A DataLoader instance for the model to be tested on.\n",
        "    loss_fn: A PyTorch loss function to calculate loss on the test data.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A tuple of testing loss and testing accuracy metrics.\n",
        "    In the form (test_loss, test_accuracy). For example:\n",
        "\n",
        "    (0.0223, 0.8985)\n",
        "  \"\"\"\n",
        "  # Put model in eval mode\n",
        "  model.eval() \n",
        "\n",
        "  # Setup test loss and test accuracy values\n",
        "  test_loss, test_acc = 0, 0\n",
        "\n",
        "  # Loop through data loader data batches\n",
        "  progress_bar = tqdm(\n",
        "      enumerate(dataloader), \n",
        "      desc=f\"Testing Epoch {epoch}\", \n",
        "      total=len(dataloader),\n",
        "      disable=disable_progress_bar\n",
        "  )\n",
        "\n",
        "  # Turn on inference context manager\n",
        "  with torch.no_grad(): # no_grad() required for PyTorch 2.0, I found some errors with `torch.inference_mode()`, please let me know if this is not the case\n",
        "      # Loop through DataLoader batches\n",
        "      for batch, (X, y) in progress_bar:\n",
        "          # Send data to target device\n",
        "          X, y = X.to(device), y.to(device)\n",
        "\n",
        "          # 1. Forward pass\n",
        "          test_pred_logits = model(X)\n",
        "\n",
        "          # 2. Calculate and accumulate loss\n",
        "          loss = loss_fn(test_pred_logits, y)\n",
        "          test_loss += loss.item()\n",
        "\n",
        "          # Calculate and accumulate accuracy\n",
        "          test_pred_labels = test_pred_logits.argmax(dim=1)\n",
        "          test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))\n",
        "\n",
        "          # Update progress bar\n",
        "          progress_bar.set_postfix(\n",
        "              {\n",
        "                  \"test_loss\": test_loss / (batch + 1),\n",
        "                  \"test_acc\": test_acc / (batch + 1),\n",
        "              }\n",
        "          )\n",
        "\n",
        "  # Adjust metrics to get average loss and accuracy per batch \n",
        "  test_loss = test_loss / len(dataloader)\n",
        "  test_acc = test_acc / len(dataloader)\n",
        "  return test_loss, test_acc\n",
        "\n",
        "def train(model: torch.nn.Module, \n",
        "          train_dataloader: torch.utils.data.DataLoader, \n",
        "          test_dataloader: torch.utils.data.DataLoader, \n",
        "          optimizer: torch.optim.Optimizer,\n",
        "          loss_fn: torch.nn.Module,\n",
        "          epochs: int,\n",
        "          device: torch.device,\n",
        "          disable_progress_bar: bool = False) -> Dict[str, List]:\n",
        "  \"\"\"Trains and tests a PyTorch model.\n",
        "\n",
        "  Passes a target PyTorch models through train_step() and test_step()\n",
        "  functions for a number of epochs, training and testing the model\n",
        "  in the same epoch loop.\n",
        "\n",
        "  Calculates, prints and stores evaluation metrics throughout.\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be trained and tested.\n",
        "    train_dataloader: A DataLoader instance for the model to be trained on.\n",
        "    test_dataloader: A DataLoader instance for the model to be tested on.\n",
        "    optimizer: A PyTorch optimizer to help minimize the loss function.\n",
        "    loss_fn: A PyTorch loss function to calculate loss on both datasets.\n",
        "    epochs: An integer indicating how many epochs to train for.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A dictionary of training and testing loss as well as training and\n",
        "    testing accuracy metrics. Each metric has a value in a list for \n",
        "    each epoch.\n",
        "    In the form: {train_loss: [...],\n",
        "                  train_acc: [...],\n",
        "                  test_loss: [...],\n",
        "                  test_acc: [...]} \n",
        "    For example if training for epochs=2: \n",
        "                 {train_loss: [2.0616, 1.0537],\n",
        "                  train_acc: [0.3945, 0.3945],\n",
        "                  test_loss: [1.2641, 1.5706],\n",
        "                  test_acc: [0.3400, 0.2973]} \n",
        "  \"\"\"\n",
        "  # Create empty results dictionary\n",
        "  results = {\"train_loss\": [],\n",
        "      \"train_acc\": [],\n",
        "      \"test_loss\": [],\n",
        "      \"test_acc\": [],\n",
        "      \"train_epoch_time\": [],\n",
        "      \"test_epoch_time\": []\n",
        "  }\n",
        "\n",
        "  # Loop through training and testing steps for a number of epochs\n",
        "  for epoch in tqdm(range(epochs), disable=disable_progress_bar):\n",
        "\n",
        "      # Perform training step and time it\n",
        "      train_epoch_start_time = time.time()\n",
        "      train_loss, train_acc = train_step(epoch=epoch, \n",
        "                                        model=model,\n",
        "                                        dataloader=train_dataloader,\n",
        "                                        loss_fn=loss_fn,\n",
        "                                        optimizer=optimizer,\n",
        "                                        device=device,\n",
        "                                        disable_progress_bar=disable_progress_bar)\n",
        "      train_epoch_end_time = time.time()\n",
        "      train_epoch_time = train_epoch_end_time - train_epoch_start_time\n",
        "      \n",
        "      # Perform testing step and time it\n",
        "      test_epoch_start_time = time.time()\n",
        "      test_loss, test_acc = test_step(epoch=epoch,\n",
        "                                      model=model,\n",
        "                                      dataloader=test_dataloader,\n",
        "                                      loss_fn=loss_fn,\n",
        "                                      device=device,\n",
        "                                      disable_progress_bar=disable_progress_bar)\n",
        "      test_epoch_end_time = time.time()\n",
        "      test_epoch_time = test_epoch_end_time - test_epoch_start_time\n",
        "\n",
        "      # Print out what's happening\n",
        "      print(\n",
        "          f\"Epoch: {epoch+1} | \"\n",
        "          f\"train_loss: {train_loss:.4f} | \"\n",
        "          f\"train_acc: {train_acc:.4f} | \"\n",
        "          f\"test_loss: {test_loss:.4f} | \"\n",
        "          f\"test_acc: {test_acc:.4f} | \"\n",
        "          f\"train_epoch_time: {train_epoch_time:.4f} | \"\n",
        "          f\"test_epoch_time: {test_epoch_time:.4f}\"\n",
        "      )\n",
        "\n",
        "      # Update results dictionary\n",
        "      results[\"train_loss\"].append(train_loss)\n",
        "      results[\"train_acc\"].append(train_acc)\n",
        "      results[\"test_loss\"].append(test_loss)\n",
        "      results[\"test_acc\"].append(test_acc)\n",
        "      results[\"train_epoch_time\"].append(train_epoch_time)\n",
        "      results[\"test_epoch_time\"].append(test_epoch_time)\n",
        "\n",
        "  # Return the filled results at the end of the epochs\n",
        "  return results"
      ],
      "metadata": {
        "id": "A4TBFLJJ7qsP"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Time models across a single run\n",
        "\n",
        "Experiment 1: single run without `torch.compile()` for 5 epochs"
      ],
      "metadata": {
        "id": "XmUOesm49nTv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Experiment 1 - Single run, no compile"
      ],
      "metadata": {
        "id": "x6YjAL_l-O29"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the number of epochs\n",
        "NUM_EPOCHS = 5\n",
        "\n",
        "# Set the learning rate as a constant\n",
        "LEARNING_RATE = 0.003"
      ],
      "metadata": {
        "id": "zjXYNZkr-1gi"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** Depending on your GPU/machine, the following code may take a while to run. E.g. in my experience on an A100, it takes about ~7 minutes."
      ],
      "metadata": {
        "id": "reKwUig7_xCs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model\n",
        "model, _ = create_model()\n",
        "model.to(device)\n",
        "\n",
        "# Create loss function and optimizer\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(),\n",
        "                             lr=LEARNING_RATE)\n",
        "\n",
        "# Train model and track the results\n",
        "single_run_no_compile_results = train(model=model,\n",
        "                                      train_dataloader=train_dataloader,\n",
        "                                      test_dataloader=test_dataloader,\n",
        "                                      loss_fn=loss_fn,\n",
        "                                      optimizer=optimizer,\n",
        "                                      epochs=NUM_EPOCHS,\n",
        "                                      device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458,
          "referenced_widgets": [
            "c7999495791642d99cc0704556ce2f1c",
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        },
        "id": "p8-mmwcc-_Lq",
        "outputId": "442df4d4-d23b-413a-b89d-f6a8d96f2816"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7999495791642d99cc0704556ce2f1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5bf649696df4619b903fe664bbe9c68"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 0:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8247d37342994ec3870dced9a0e6af5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.7612 | train_acc: 0.7361 | test_loss: 0.6752 | test_acc: 0.7738 | train_epoch_time: 75.7051 | test_epoch_time: 7.6668\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38e778c05af54d0ab74ea83fe7484aba"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 1:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4578fc8c83bb4444b7cfda7f26940528"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 | train_loss: 0.4214 | train_acc: 0.8545 | test_loss: 0.4838 | test_acc: 0.8338 | train_epoch_time: 71.1667 | test_epoch_time: 7.5469\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff8ba34cca0c40f2978441b6f7ed2647"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 2:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7daaf97100954494b31cc86cdbc937d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 | train_loss: 0.2986 | train_acc: 0.8959 | test_loss: 0.4052 | test_acc: 0.8618 | train_epoch_time: 71.2197 | test_epoch_time: 7.4989\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe7dcdd6571147118b3e690773386d3c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 3:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8290bae08d3e458f9b85d474c2d1ab3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 | train_loss: 0.2270 | train_acc: 0.9201 | test_loss: 0.4700 | test_acc: 0.8494 | train_epoch_time: 71.3631 | test_epoch_time: 7.9202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70315d568e804cf8b486390a7790be34"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 4:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78145c2f1b184c4b9c9c3ece9aae9b1a"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 | train_loss: 0.1704 | train_acc: 0.9397 | test_loss: 0.3665 | test_acc: 0.8804 | train_epoch_time: 71.3793 | test_epoch_time: 7.7546\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Experiment 2, single, using `torch.compile()`\n",
        "\n",
        "Same setup as experiment 1 except with the new line `torch.compile()`."
      ],
      "metadata": {
        "id": "nxJK6xXbDQ_7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model and transforms\n",
        "model, _ = create_model()\n",
        "model.to(device)\n",
        "\n",
        "# Create loss function and optimizer\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(),\n",
        "                             lr=LEARNING_RATE)\n",
        "\n",
        "# Compile the model (requires PyTorch 2.0+)\n",
        "import time\n",
        "compile_start_time = time.time()\n",
        "\n",
        "### New in PyTorch 2.x ###\n",
        "compile_model = torch.compile(model) # NOTE: this should ideally be \"compiled_model\" not \"compile_model\"\n",
        "compile_model.to(device) # make sure compiled on device\n",
        "\n",
        "compile_end_time = time.time()\n",
        "compile_time = compile_end_time - compile_start_time\n",
        "\n",
        "print(f\"Time to compile: {compile_time} |\\nNote: the first time you compile a model/train a compiled model, the first epoch may take longer due to optimizations happening behind the scenes\")\n",
        "\n",
        "# Train the compiled model\n",
        "single_run_compile_results = train(model=compile_model, # NOTE: this is an error! (leaving it in for future reference)\n",
        "                                   train_dataloader=train_dataloader,\n",
        "                                   test_dataloader=test_dataloader,\n",
        "                                   loss_fn=loss_fn,\n",
        "                                   optimizer=optimizer,\n",
        "                                   epochs=NUM_EPOCHS,\n",
        "                                   device=device)"
      ],
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        "id": "8i12hOmTCyB0",
        "outputId": "f26b4dd1-caf1-451a-ccb5-846834bc5df9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to compile: 0.0066258907318115234 |\n",
            "Note: the first time you compile a model/train a compiled model, the first epoch may take longer due to optimizations happening behind the scenes\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 0:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.7831 | train_acc: 0.7294 | test_loss: 0.7470 | test_acc: 0.7410 | train_epoch_time: 118.4845 | test_epoch_time: 25.6489\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 1:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 | train_loss: 0.4346 | train_acc: 0.8508 | test_loss: 0.5180 | test_acc: 0.8230 | train_epoch_time: 64.4995 | test_epoch_time: 7.1518\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 2:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80ec12a2d10248eda3591539c58523b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 | train_loss: 0.3171 | train_acc: 0.8909 | test_loss: 0.5831 | test_acc: 0.8085 | train_epoch_time: 64.7863 | test_epoch_time: 7.1712\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb93966d27574d2fbeec8431bc229b92"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 3:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 | train_loss: 0.2345 | train_acc: 0.9196 | test_loss: 0.4294 | test_acc: 0.8495 | train_epoch_time: 64.5203 | test_epoch_time: 7.1254\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3293a3dca4cc4164b4d9871dfea31629"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 4:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95c695f0983f4aa3b407df89d1751d7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 | train_loss: 0.1723 | train_acc: 0.9399 | test_loss: 0.3651 | test_acc: 0.8841 | train_epoch_time: 64.4010 | test_epoch_time: 7.2215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model and transforms\n",
        "model, transforms = create_model()\n",
        "model.to(device)\n",
        "\n",
        "# Create loss function and optimizer\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(),\n",
        "                             lr=LEARNING_RATE)\n",
        "\n",
        "# Compile the model and time how long it takes\n",
        "compile_start_time = time.time()\n",
        "\n",
        "### New in PyTorch 2.x ###\n",
        "compiled_model = torch.compile(model)\n",
        "##########################\n",
        "\n",
        "compile_end_time = time.time()\n",
        "compile_time = compile_end_time - compile_start_time\n",
        "print(f\"Time to compile: {compile_time} | Note: The first time you compile your model, the first few epochs will be slower than subsequent runs.\")\n",
        "\n",
        "# Train the compiled model\n",
        "single_run_compile_results = train(model=compiled_model,\n",
        "                                   train_dataloader=train_dataloader,\n",
        "                                   test_dataloader=test_dataloader,\n",
        "                                   loss_fn=loss_fn,\n",
        "                                   optimizer=optimizer,\n",
        "                                   epochs=NUM_EPOCHS,\n",
        "                                   device=device)"
      ],
      "metadata": {
        "id": "r5EzRzmFEUgQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Compare the results of experiment 1 and 2"
      ],
      "metadata": {
        "id": "aMN8IVfrGp21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn experiment results into dataframes\n",
        "import pandas as pd\n",
        "single_run_no_compile_results_df = pd.DataFrame(single_run_no_compile_results)\n",
        "single_run_compile_results_df = pd.DataFrame(single_run_compile_results) "
      ],
      "metadata": {
        "id": "k-Rl6jorJvwf"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "single_run_no_compile_results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "CbuaIRU4J9g0",
        "outputId": "3ccbc6a9-fdd2-4c2f-bcd9-6fd4127983fc"
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      "execution_count": 33,
      "outputs": [
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              "   train_loss  train_acc  test_loss  test_acc  train_epoch_time  \\\n",
              "0    0.761156   0.736069   0.675165  0.773833         75.705132   \n",
              "1    0.421439   0.854536   0.483774  0.833762         71.166738   \n",
              "2    0.298607   0.895896   0.405220  0.861847         71.219677   \n",
              "3    0.227025   0.920137   0.470029  0.849387         71.363075   \n",
              "4    0.170397   0.939738   0.366482  0.880439         71.379308   \n",
              "\n",
              "   test_epoch_time  \n",
              "0         7.666803  \n",
              "1         7.546933  \n",
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              "\n",
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          "height": 206
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          "data": {
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              "   train_loss  train_acc  test_loss  test_acc  train_epoch_time  \\\n",
              "0    0.783119   0.729360   0.746991  0.741001        118.484514   \n",
              "1    0.434553   0.850823   0.518003  0.822983         64.499461   \n",
              "2    0.317094   0.890861   0.583142  0.808544         64.786344   \n",
              "3    0.234511   0.919625   0.429354  0.849486         64.520294   \n",
              "4    0.172298   0.939878   0.365066  0.884098         64.401047   \n",
              "\n",
              "   test_epoch_time  \n",
              "0        25.648897  \n",
              "1         7.151754  \n",
              "2         7.171236  \n",
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              "      <td>0.729360</td>\n",
              "      <td>0.746991</td>\n",
              "      <td>0.741001</td>\n",
              "      <td>118.484514</td>\n",
              "      <td>25.648897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.434553</td>\n",
              "      <td>0.850823</td>\n",
              "      <td>0.518003</td>\n",
              "      <td>0.822983</td>\n",
              "      <td>64.499461</td>\n",
              "      <td>7.151754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.317094</td>\n",
              "      <td>0.890861</td>\n",
              "      <td>0.583142</td>\n",
              "      <td>0.808544</td>\n",
              "      <td>64.786344</td>\n",
              "      <td>7.171236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.234511</td>\n",
              "      <td>0.919625</td>\n",
              "      <td>0.429354</td>\n",
              "      <td>0.849486</td>\n",
              "      <td>64.520294</td>\n",
              "      <td>7.125437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.172298</td>\n",
              "      <td>0.939878</td>\n",
              "      <td>0.365066</td>\n",
              "      <td>0.884098</td>\n",
              "      <td>64.401047</td>\n",
              "      <td>7.221537</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91b3b5c9-63e3-4788-b7f2-b5215b57611b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-91b3b5c9-63e3-4788-b7f2-b5215b57611b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91b3b5c9-63e3-4788-b7f2-b5215b57611b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create filename to save the results\n",
        "DATASET_NAME = \"CIFAR10\"\n",
        "MODEL_NAME = \"ResNet50\""
      ],
      "metadata": {
        "id": "qk1FFPy4J_p7"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def plot_mean_epoch_times(non_compiled_results: pd.DataFrame, \n",
        "                          compiled_results: pd.DataFrame, \n",
        "                          multi_runs: bool=False, \n",
        "                          num_runs: int=0, \n",
        "                          save: bool=False, \n",
        "                          save_path: str=\"\",\n",
        "                          dataset_name: str=DATASET_NAME,\n",
        "                          model_name: str=MODEL_NAME,\n",
        "                          num_epochs: int=NUM_EPOCHS,\n",
        "                          image_size: int=IMAGE_SIZE,\n",
        "                          batch_size: int=BATCH_SIZE) -> plt.figure:\n",
        "    \n",
        "    # Get the mean epoch times from the non-compiled models\n",
        "    mean_train_epoch_time = non_compiled_results.train_epoch_time.mean()\n",
        "    mean_test_epoch_time = non_compiled_results.test_epoch_time.mean()\n",
        "    mean_results = [mean_train_epoch_time, mean_test_epoch_time]\n",
        "\n",
        "    # Get the mean epoch times from the compiled models\n",
        "    mean_compile_train_epoch_time = compiled_results.train_epoch_time.mean()\n",
        "    mean_compile_test_epoch_time = compiled_results.test_epoch_time.mean()\n",
        "    mean_compile_results = [mean_compile_train_epoch_time, mean_compile_test_epoch_time]\n",
        "\n",
        "    # Calculate the percentage difference between the mean compile and non-compile train epoch times\n",
        "    train_epoch_time_diff = mean_compile_train_epoch_time - mean_train_epoch_time\n",
        "    train_epoch_time_diff_percent = (train_epoch_time_diff / mean_train_epoch_time) * 100\n",
        "\n",
        "    # Calculate the percentage difference between the mean compile and non-compile test epoch times\n",
        "    test_epoch_time_diff = mean_compile_test_epoch_time - mean_test_epoch_time\n",
        "    test_epoch_time_diff_percent = (test_epoch_time_diff / mean_test_epoch_time) * 100\n",
        "\n",
        "    # Print the mean difference percentages\n",
        "    print(f\"Mean train epoch time difference: {round(train_epoch_time_diff_percent, 3)}% (negative means faster)\")\n",
        "    print(f\"Mean test epoch time difference: {round(test_epoch_time_diff_percent, 3)}% (negative means faster)\")\n",
        "\n",
        "    # Create a bar plot of the mean train and test epoch time for both compiled and non-compiled models\n",
        "    plt.figure(figsize=(10, 7))\n",
        "    width = 0.3\n",
        "    x_indicies = np.arange(len(mean_results))\n",
        "\n",
        "    plt.bar(x=x_indicies, height=mean_results, width=width, label=\"non_compiled_results\")\n",
        "    plt.bar(x=x_indicies + width, height=mean_compile_results, width=width, label=\"compiled_results\")\n",
        "    plt.xticks(x_indicies + width / 2, (\"Train Epoch\", \"Test Epoch\"))\n",
        "    plt.ylabel(\"Mean epoch time (seconds, lower is better)\")\n",
        "\n",
        "    # Create the title based on the parameters passed to the function\n",
        "    if multi_runs:\n",
        "        plt.suptitle(\"Multiple run results\")\n",
        "        plt.title(f\"GPU: {gpu_name} | Epochs: {num_epochs} ({num_runs} runs) | Data: {dataset_name} | Model: {model_name} | Image size: {image_size} | Batch size: {batch_size}\")\n",
        "    else:\n",
        "        plt.suptitle(\"Single run results\")\n",
        "        plt.title(f\"GPU: {gpu_name} | Epochs: {num_epochs} | Data: {dataset_name} | Model: {model_name} | Image size: {image_size} | Batch size: {batch_size}\")\n",
        "    plt.legend();\n",
        "\n",
        "    # Save the figure\n",
        "    if save:\n",
        "        assert save_path != \"\", \"Please specify a save path to save the model figure to via the save_path parameter.\"\n",
        "        plt.savefig(save_path)\n",
        "        print(f\"[INFO] Plot saved to {save_path}\")"
      ],
      "metadata": {
        "id": "uMFLJgP7KVTx"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create directory for saving figures\n",
        "import os\n",
        "dir_to_save_figures_in = \"pytorch_2_results/figures/\"\n",
        "os.makedirs(dir_to_save_figures_in, exist_ok=True)\n",
        "\n",
        "# Create a save path for the single run results\n",
        "save_path_single_run = f\"{dir_to_save_figures_in}single_run_{GPU_NAME}_{MODEL_NAME}_{DATASET_NAME}_{IMAGE_SIZE}_train_epoch_time.png\"\n",
        "print(f\"[INFO] Save path for single run results: {save_path_single_run}\")\n",
        "\n",
        "# Plot the results and save the figure(s)\n",
        "plot_mean_epoch_times(non_compiled_results=single_run_no_compile_results_df,\n",
        "                      compiled_results=single_run_compile_results_df,\n",
        "                      multi_runs=False,\n",
        "                      save_path=save_path_single_run,\n",
        "                      save=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "-snaxgsWKu4Q",
        "outputId": "675875bc-ba8f-4ee8-a76c-0d817e857d5c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Save path for single run results: pytorch_2_results/figures/single_run_NVIDIA_A100-SXM4-40GB_ResNet50_CIFAR10_224_train_epoch_time.png\n",
            "Mean train epoch time difference: 4.395% (negative means faster)\n",
            "Mean test epoch time difference: 41.502% (negative means faster)\n",
            "[INFO] Plot saved to pytorch_2_results/figures/single_run_NVIDIA_A100-SXM4-40GB_ResNet50_CIFAR10_224_train_epoch_time.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4 Save single run results to file with GPU details"
      ],
      "metadata": {
        "id": "ieihM1Z3LBpw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a directory for single_run results\n",
        "import os\n",
        "pytorch_2_results_dir = \"pytorch_2_results\"\n",
        "pytorch_2_single_run_results_dir = f\"{pytorch_2_results_dir}/single_run_results\"\n",
        "os.makedirs(pytorch_2_single_run_results_dir, exist_ok=True)\n",
        "\n",
        "# Create filenames for each of the dataframes\n",
        "save_name_for_non_compiled_results = f\"single_run_non_compiled_results_{DATASET_NAME}_{MODEL_NAME}_{GPU_NAME}.csv\"\n",
        "save_name_for_compiled_results = f\"single_run_compiled_results_{DATASET_NAME}_{MODEL_NAME}_{GPU_NAME}.csv\"\n",
        "\n",
        "# Create filepaths to save the results to\n",
        "single_run_no_compile_save_path = f\"{pytorch_2_single_run_results_dir}/{save_name_for_non_compiled_results}\"\n",
        "single_run_compile_save_path = f\"{pytorch_2_single_run_results_dir}/{save_name_for_compiled_results}\"\n",
        "print(f\"[INFO] Saving non-compiled experiment 1 results to: {single_run_no_compile_save_path}\")\n",
        "print(f\"[INFO] Saving compiled experiment 2 results to: {single_run_compile_save_path}\")\n",
        "\n",
        "# Save the results\n",
        "single_run_no_compile_results_df.to_csv(single_run_no_compile_save_path)\n",
        "single_run_compile_results_df.to_csv(single_run_compile_save_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMnVlM4_NCFY",
        "outputId": "ee1fd37b-5c3d-4ebf-d4e1-58731c406f36"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving non-compiled experiment 1 results to: pytorch_2_results/single_run_results/single_run_non_compiled_results_CIFAR10_ResNet50_NVIDIA_A100-SXM4-40GB.csv\n",
            "[INFO] Saving compiled experiment 2 results to: pytorch_2_results/single_run_results/single_run_compiled_results_CIFAR10_ResNet50_NVIDIA_A100-SXM4-40GB.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Time models across multiple runs\n",
        "\n",
        "Time for multi-run experiments!\n",
        "\n",
        "* Experiment 3 - 3x5 epochs without `torch.compile()`\n",
        "* Experiment 4 - 3x5 epochs with `torch.compile()`\n",
        "\n",
        "Before running experiment 3 and 4, let's create 3 functions:\n",
        "\n",
        "1. **Experiment 3:** `create_and_train_non_compiled_model()` - creates and trains a model for a single run (can put this function in a loop for mulitple runs).\n",
        "2. **Experiment 4:** `create_compiled_model()` - creates and compiles a model, returns the compiled model.\n",
        "3. **Experiment 4:** `train_compiled_model()` - trains a compiled model for a single run (can put this in a loop to train for multiple runs)."
      ],
      "metadata": {
        "id": "sx5sA4BfNUxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_and_train_non_compiled_model(epochs=NUM_EPOCHS,\n",
        "                                        learning_rate=LEARNING_RATE,\n",
        "                                        disable_progress_bar=False): \n",
        "\n",
        "    \"\"\"\n",
        "    Create and train a non-compiled PyTorch model.\n",
        "    \"\"\"\n",
        "    model, _ = create_model()\n",
        "    model.to(device)\n",
        "\n",
        "    loss_fn = torch.nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.Adam(model.parameters(),\n",
        "                                 lr=learning_rate)\n",
        "    \n",
        "    results = train(model=model,\n",
        "                    train_dataloader=train_dataloader,\n",
        "                    test_dataloader=test_dataloader,\n",
        "                    loss_fn=loss_fn,\n",
        "                    optimizer=optimizer,\n",
        "                    epochs=epochs,\n",
        "                    device=device,\n",
        "                    disable_progress_bar=disable_progress_bar)\n",
        "    \n",
        "    return results\n",
        "\n",
        "def create_compiled_model():\n",
        "  \"\"\"\n",
        "  Create a compiled PyTorch model and return it.\n",
        "  \"\"\" \n",
        "  model, _ = create_model()\n",
        "  model.to(device)\n",
        "\n",
        "  compile_start_time = time.time()\n",
        "\n",
        "  ### New in PyTorch 2.0!!! ###\n",
        "  compiled_model = torch.compile(model)\n",
        "\n",
        "  compile_end_time = time.time()\n",
        "\n",
        "  compile_time = compile_end_time - compile_start_time\n",
        "\n",
        "  print(f\"[INFO] Model compile time: {compile_time}\")\n",
        "\n",
        "  return compiled_model\n",
        "\n",
        "def train_compiled_model(model=compiled_model,\n",
        "                         epochs=NUM_EPOCHS,\n",
        "                         learning_rate=LEARNING_RATE,\n",
        "                         disable_progress_bar=False):\n",
        "  \"\"\"\n",
        "  Train a compiled model and return the results.\n",
        "  \"\"\" \n",
        "  loss_fn = torch.nn.CrossEntropyLoss()\n",
        "  optimizer = torch.optim.Adam(compiled_model.parameters(),\n",
        "                               lr=learning_rate)\n",
        "  \n",
        "  compile_results = train(model=model,\n",
        "                          train_dataloader=train_dataloader,\n",
        "                          test_dataloader=test_dataloader,\n",
        "                          loss_fn=loss_fn,\n",
        "                          optimizer=optimizer,\n",
        "                          epochs=epochs,\n",
        "                          device=device,\n",
        "                          disable_progress_bar=disable_progress_bar)\n",
        "\n",
        "  return compile_results\n"
      ],
      "metadata": {
        "id": "EvsCORRo7pNI"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Experiment 3 - Multiple runs, no compile\n",
        "\n",
        "**Note:** Because we are running multiple runs, the code below may take a while to run. If one single run take ~7 minutes on an A100, the following code could take 20 minutes on an A100. \n",
        "\n",
        "> One of the most painful things of machine learning is that models take a while to train and one of the most beautiful things of machine learning is that models take a while to train. So plenty of time to go for a walk."
      ],
      "metadata": {
        "id": "G7j5780W9Ru0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run non-compiled model for multiple runs\n",
        "NUM_RUNS = 3\n",
        "NUM_EPOCHS = 5\n",
        "\n",
        "# Create an empty list to store multiple run results\n",
        "non_compile_results_multiple_runs = []\n",
        "\n",
        "# Run non-compiled model for multiple runs\n",
        "for i in tqdm(range(NUM_RUNS)):\n",
        "  print(f\"[INFO] Run {i+1} of {NUM_RUNS} for non-compiled model\")\n",
        "  results = create_and_train_non_compiled_model(epochs=NUM_EPOCHS, disable_progress_bar=True)\n",
        "  non_compile_results_multiple_runs.append(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370,
          "referenced_widgets": [
            "bb96b225c81747669a57569622a17a78",
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            "5a663c00dfd84cae8bdeb0738c0c4020"
          ]
        },
        "id": "WwS_A_cN-_uV",
        "outputId": "37258ee6-d07b-4f21-fef0-47083cc25238"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb96b225c81747669a57569622a17a78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Run 1 of 3 for non-compiled model\n",
            "Epoch: 1 | train_loss: 0.7901 | train_acc: 0.7267 | test_loss: 0.6552 | test_acc: 0.7737 | train_epoch_time: 70.4700 | test_epoch_time: 7.6880\n",
            "Epoch: 2 | train_loss: 0.4430 | train_acc: 0.8489 | test_loss: 0.5212 | test_acc: 0.8210 | train_epoch_time: 70.4249 | test_epoch_time: 7.9523\n",
            "Epoch: 3 | train_loss: 0.3196 | train_acc: 0.8905 | test_loss: 0.4269 | test_acc: 0.8580 | train_epoch_time: 70.5179 | test_epoch_time: 7.5705\n",
            "Epoch: 4 | train_loss: 0.2378 | train_acc: 0.9174 | test_loss: 0.3968 | test_acc: 0.8716 | train_epoch_time: 70.3693 | test_epoch_time: 7.6105\n",
            "Epoch: 5 | train_loss: 0.1776 | train_acc: 0.9381 | test_loss: 0.4447 | test_acc: 0.8690 | train_epoch_time: 70.4685 | test_epoch_time: 7.5161\n",
            "[INFO] Run 2 of 3 for non-compiled model\n",
            "Epoch: 1 | train_loss: 0.8053 | train_acc: 0.7198 | test_loss: 0.6471 | test_acc: 0.7709 | train_epoch_time: 70.9584 | test_epoch_time: 7.5721\n",
            "Epoch: 2 | train_loss: 0.4451 | train_acc: 0.8475 | test_loss: 0.5613 | test_acc: 0.8169 | train_epoch_time: 70.4403 | test_epoch_time: 7.4987\n",
            "Epoch: 3 | train_loss: 0.3173 | train_acc: 0.8916 | test_loss: 0.4261 | test_acc: 0.8548 | train_epoch_time: 70.4179 | test_epoch_time: 7.6314\n",
            "Epoch: 4 | train_loss: 0.2412 | train_acc: 0.9165 | test_loss: 0.4067 | test_acc: 0.8668 | train_epoch_time: 70.5341 | test_epoch_time: 7.7344\n",
            "Epoch: 5 | train_loss: 0.1729 | train_acc: 0.9392 | test_loss: 0.3699 | test_acc: 0.8767 | train_epoch_time: 70.7180 | test_epoch_time: 7.7019\n",
            "[INFO] Run 3 of 3 for non-compiled model\n",
            "Epoch: 1 | train_loss: 0.7796 | train_acc: 0.7280 | test_loss: 1.0861 | test_acc: 0.7201 | train_epoch_time: 70.2628 | test_epoch_time: 7.6740\n",
            "Epoch: 2 | train_loss: 0.4326 | train_acc: 0.8505 | test_loss: 0.6013 | test_acc: 0.7965 | train_epoch_time: 70.4564 | test_epoch_time: 7.5600\n",
            "Epoch: 3 | train_loss: 0.3124 | train_acc: 0.8926 | test_loss: 0.4560 | test_acc: 0.8483 | train_epoch_time: 70.3929 | test_epoch_time: 7.5944\n",
            "Epoch: 4 | train_loss: 0.2347 | train_acc: 0.9183 | test_loss: 0.3845 | test_acc: 0.8734 | train_epoch_time: 70.5535 | test_epoch_time: 7.4801\n",
            "Epoch: 5 | train_loss: 0.1728 | train_acc: 0.9396 | test_loss: 0.4755 | test_acc: 0.8475 | train_epoch_time: 70.4916 | test_epoch_time: 7.7536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Go through the non_compile_results_multiple_runs and create a dataframe for each and then combine each dataframe\n",
        "non_compile_results_dfs = []\n",
        "for result in non_compile_results_multiple_runs:\n",
        "  result_df = pd.DataFrame(result)\n",
        "  non_compile_results_dfs.append(result_df)\n",
        "non_compile_results_multiple_runs_df = pd.concat(non_compile_results_dfs)\n",
        "\n",
        "# Get the average results across the board\n",
        "non_compile_results_multiple_runs_df = non_compile_results_multiple_runs_df.groupby(non_compile_results_multiple_runs_df.index).mean()\n",
        "non_compile_results_multiple_runs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7-8tE8PnATEB",
        "outputId": "17efd7d7-30fd-484a-99fe-8134b4ebf744"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc  train_epoch_time  \\\n",
              "0    0.791669   0.724828   0.796088  0.754912         70.563755   \n",
              "1    0.440249   0.849002   0.561259  0.811445         70.440525   \n",
              "2    0.316451   0.891565   0.436321  0.853705         70.442901   \n",
              "3    0.237918   0.917394   0.395977  0.870616         70.485643   \n",
              "4    0.174429   0.938939   0.430049  0.864386         70.559365   \n",
              "\n",
              "   test_epoch_time  \n",
              "0         7.644714  \n",
              "1         7.670343  \n",
              "2         7.598755  \n",
              "3         7.608333  \n",
              "4         7.657195  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b469e90e-fb21-4f92-a823-b27b97e3c2f3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>train_epoch_time</th>\n",
              "      <th>test_epoch_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.791669</td>\n",
              "      <td>0.724828</td>\n",
              "      <td>0.796088</td>\n",
              "      <td>0.754912</td>\n",
              "      <td>70.563755</td>\n",
              "      <td>7.644714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.440249</td>\n",
              "      <td>0.849002</td>\n",
              "      <td>0.561259</td>\n",
              "      <td>0.811445</td>\n",
              "      <td>70.440525</td>\n",
              "      <td>7.670343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.316451</td>\n",
              "      <td>0.891565</td>\n",
              "      <td>0.436321</td>\n",
              "      <td>0.853705</td>\n",
              "      <td>70.442901</td>\n",
              "      <td>7.598755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.237918</td>\n",
              "      <td>0.917394</td>\n",
              "      <td>0.395977</td>\n",
              "      <td>0.870616</td>\n",
              "      <td>70.485643</td>\n",
              "      <td>7.608333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.174429</td>\n",
              "      <td>0.938939</td>\n",
              "      <td>0.430049</td>\n",
              "      <td>0.864386</td>\n",
              "      <td>70.559365</td>\n",
              "      <td>7.657195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b469e90e-fb21-4f92-a823-b27b97e3c2f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b469e90e-fb21-4f92-a823-b27b97e3c2f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b469e90e-fb21-4f92-a823-b27b97e3c2f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Experiment 4, multiple runs, with compile"
      ],
      "metadata": {
        "id": "X3MUkCkTE34P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create compiled model\n",
        "compiled_model = create_compiled_model()\n",
        "\n",
        "# Create an empty list to store compiled model result runs\n",
        "compiled_results_multiple_runs = []\n",
        "\n",
        "# Run compiled model for multiple training runs\n",
        "for i in tqdm(range(NUM_RUNS)):\n",
        "  print(f\"[INFO] Run {i+1} of {NUM_RUNS} for compiled model\")\n",
        "  # Train compiled model and append results (note: model will only be compiled once at the start of the first epoch of the first run)\n",
        "  results = train_compiled_model(model=compiled_model, \n",
        "                                 epochs=NUM_EPOCHS,\n",
        "                                 disable_progress_bar=False)\n",
        "  compiled_results_multiple_runs.append(results)"
      ],
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        },
        "id": "wCWqLhclF457",
        "outputId": "e06dcfe1-1b90-4fd4-95a8-96d862857d55"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Model compile time: 0.0026314258575439453\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "404c361291ce4abca584914c4118ba81"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Run 1 of 3 for compiled model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6197001d4eeb4a1cb2e1b87033c39c8c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0cbbc6a754142ad9135308143cde129"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 0:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4aab933441340af83631d4772ba498c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.7959 | train_acc: 0.7234 | test_loss: 0.7443 | test_acc: 0.7413 | train_epoch_time: 103.3092 | test_epoch_time: 22.3508\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dafaf2a50bca46099b515e2c98c386e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 1:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d781ebb4e7a64bf8912f8ecaf5308060"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 | train_loss: 0.4377 | train_acc: 0.8504 | test_loss: 0.5481 | test_acc: 0.8118 | train_epoch_time: 63.8893 | test_epoch_time: 7.2228\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf9810959dfd428a8bbe895f78150c20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 2:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "808c290036394db38f3c1d0a8eb586be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 | train_loss: 0.3152 | train_acc: 0.8920 | test_loss: 0.5006 | test_acc: 0.8373 | train_epoch_time: 64.1252 | test_epoch_time: 6.9573\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92707df1d9c24e3d992ae58fa19c17e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 3:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60d4294cae19446789f3167a7a9d46aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 | train_loss: 0.2342 | train_acc: 0.9190 | test_loss: 0.4468 | test_acc: 0.8478 | train_epoch_time: 64.0888 | test_epoch_time: 6.9980\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61a1b8954f5f40a899713a0b0232ae30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 4:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10ebaba6ac2944619370a473addfdf94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 | train_loss: 0.1745 | train_acc: 0.9384 | test_loss: 0.4810 | test_acc: 0.8561 | train_epoch_time: 64.0858 | test_epoch_time: 7.1799\n",
            "[INFO] Run 2 of 3 for compiled model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6990e9800e5f4287bac084ad4260755b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f22176b483ae4b8b8a9e96eb5f1e7aba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 0:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6e3eec41e5a482ab8c5b3f7ab4f50ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.1925 | train_acc: 0.9319 | test_loss: 0.3669 | test_acc: 0.8817 | train_epoch_time: 64.1221 | test_epoch_time: 6.9803\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7fb16b8e882d472cbbba47eb29ea32e5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 1:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0780d267bb444328763cae1a318c974"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 | train_loss: 0.1244 | train_acc: 0.9567 | test_loss: 0.3533 | test_acc: 0.8928 | train_epoch_time: 64.0442 | test_epoch_time: 6.8866\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da09a7645b974fe5b97aa1156592a3e8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 2:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1bb48d2f4fd4b29bd7366ab79672ba8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 | train_loss: 0.0890 | train_acc: 0.9687 | test_loss: 0.4817 | test_acc: 0.8640 | train_epoch_time: 64.0605 | test_epoch_time: 7.0766\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6661f9a9c0b4e05bb8187e191e79ad3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 3:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "838129140cd143b6b418c9ac97604575"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 | train_loss: 0.0733 | train_acc: 0.9738 | test_loss: 0.4168 | test_acc: 0.8912 | train_epoch_time: 64.0965 | test_epoch_time: 6.9628\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f23bd70a4b94587bfe85f4ab87a3298"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 4:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82afa5ca05f34f1c879fc29653457c39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 | train_loss: 0.0577 | train_acc: 0.9798 | test_loss: 0.4056 | test_acc: 0.8967 | train_epoch_time: 64.3501 | test_epoch_time: 6.9296\n",
            "[INFO] Run 3 of 3 for compiled model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f8ca4d09e444ca78071b549f37f6772"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2f6709ca9f14f34ab682afa32fca3f8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 0:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57b34981d4ee485796dfc7d4fbf40325"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.0682 | train_acc: 0.9764 | test_loss: 0.4597 | test_acc: 0.8816 | train_epoch_time: 64.3131 | test_epoch_time: 7.0507\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4758bb8128724ef4bc7ad1a95b1e9376"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 1:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "821336e06de747eba359766503b2a498"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 | train_loss: 0.0537 | train_acc: 0.9814 | test_loss: 0.4043 | test_acc: 0.8969 | train_epoch_time: 64.6288 | test_epoch_time: 6.9666\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab1e301ec5d0495f9cf1d353ed64fce6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 2:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29f18635d8b345a48a985b6f8f40cdff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 | train_loss: 0.0454 | train_acc: 0.9841 | test_loss: 0.4339 | test_acc: 0.8890 | train_epoch_time: 64.1373 | test_epoch_time: 7.0102\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9226c2b3cffc4c6e9ef803ebc75c0e4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 3:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5507748566f24dc5b11419c3d45e71a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 | train_loss: 0.0369 | train_acc: 0.9875 | test_loss: 0.4787 | test_acc: 0.8872 | train_epoch_time: 64.1874 | test_epoch_time: 6.9630\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/391 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74d95f7adff1485f991937205753eb18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing Epoch 4:   0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "daaa5534bb5c4433b062e0e5cc6be926"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 | train_loss: 0.0376 | train_acc: 0.9866 | test_loss: 0.5105 | test_acc: 0.8842 | train_epoch_time: 64.0908 | test_epoch_time: 7.1625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Want to combine all the results into a single average dataframe\n",
        "compile_results_df = []\n",
        "for result in compiled_results_multiple_runs:\n",
        "  results_df = pd.DataFrame(result)\n",
        "  compile_results_df.append(results_df)\n",
        "compile_results_multiple_runs_df = pd.concat(compile_results_df)\n",
        "\n",
        "# Get the average across all the runs for the compiled model\n",
        "compile_results_multiple_runs_df = compile_results_multiple_runs_df.groupby(compile_results_multiple_runs_df.index).mean()\n",
        "compile_results_multiple_runs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IuEC2OOSGMk4",
        "outputId": "88b2129f-d18f-41e5-d020-d1a24669711e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc  train_epoch_time  \\\n",
              "0    0.352225   0.877225   0.523651  0.834883         77.248146   \n",
              "1    0.205245   0.929492   0.435225  0.867155         64.187451   \n",
              "2    0.149879   0.948252   0.472081  0.863463         64.107671   \n",
              "3    0.114807   0.960094   0.447427  0.875396         64.124233   \n",
              "4    0.089908   0.968229   0.465696  0.878989         64.175556   \n",
              "\n",
              "   test_epoch_time  \n",
              "0        12.127237  \n",
              "1         7.025314  \n",
              "2         7.014695  \n",
              "3         6.974602  \n",
              "4         7.090651  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1858b67a-f017-46a0-990f-809b67d77b34\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>train_epoch_time</th>\n",
              "      <th>test_epoch_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.352225</td>\n",
              "      <td>0.877225</td>\n",
              "      <td>0.523651</td>\n",
              "      <td>0.834883</td>\n",
              "      <td>77.248146</td>\n",
              "      <td>12.127237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.205245</td>\n",
              "      <td>0.929492</td>\n",
              "      <td>0.435225</td>\n",
              "      <td>0.867155</td>\n",
              "      <td>64.187451</td>\n",
              "      <td>7.025314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.149879</td>\n",
              "      <td>0.948252</td>\n",
              "      <td>0.472081</td>\n",
              "      <td>0.863463</td>\n",
              "      <td>64.107671</td>\n",
              "      <td>7.014695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.114807</td>\n",
              "      <td>0.960094</td>\n",
              "      <td>0.447427</td>\n",
              "      <td>0.875396</td>\n",
              "      <td>64.124233</td>\n",
              "      <td>6.974602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.089908</td>\n",
              "      <td>0.968229</td>\n",
              "      <td>0.465696</td>\n",
              "      <td>0.878989</td>\n",
              "      <td>64.175556</td>\n",
              "      <td>7.090651</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1858b67a-f017-46a0-990f-809b67d77b34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1858b67a-f017-46a0-990f-809b67d77b34 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1858b67a-f017-46a0-990f-809b67d77b34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Compare results of experiment 3 and 4"
      ],
      "metadata": {
        "id": "X-a9aqw9L-D9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a directory to save the multi-run figure to \n",
        "os.makedirs(\"pytorch_2_results/figures\", exist_ok=True)\n",
        "\n",
        "# Create a path to save the figure for multiple runs\n",
        "save_path_multi_run = f\"pytorch_2_results/figures/multi_run_{GPU_NAME}_{MODEL_NAME}_{DATASET_NAME}_{IMAGE_SIZE}_train_epoch_time.png\"\n",
        "\n",
        "# Plot the mean epoch times for experiment 3 and 4\n",
        "plot_mean_epoch_times(non_compiled_results=non_compile_results_multiple_runs_df, \n",
        "                      compiled_results=compile_results_multiple_runs_df, \n",
        "                      multi_runs=True, \n",
        "                      num_runs=NUM_RUNS, \n",
        "                      save_path=save_path_multi_run, \n",
        "                      save=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "id": "Ett38PTiMthX",
        "outputId": "ee8ae478-47a3-4713-a29b-4ba0560a54ef"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean train epoch time difference: -5.291% (negative means faster)\n",
            "Mean test epoch time difference: 5.378% (negative means faster)\n",
            "[INFO] Plot saved to pytorch_2_results/figures/multi_run_NVIDIA_A100-SXM4-40GB_ResNet50_CIFAR10_224_train_epoch_time.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.4 Save multi runs results to file with GPU details "
      ],
      "metadata": {
        "id": "RhnNl6XEM61A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a directory for multi_run results\n",
        "import os\n",
        "pytorch_2_results_dir = \"pytorch_2_results\"\n",
        "pytorch_2_multi_run_results_dir = f\"{pytorch_2_results_dir}/multi_run_results\"\n",
        "os.makedirs(pytorch_2_multi_run_results_dir, exist_ok=True)\n",
        "\n",
        "# Create filenames for each of the dataframes\n",
        "save_name_for_multi_run_non_compiled_results = f\"multi_run_non_compiled_results_{NUM_RUNS}_runs_{DATASET_NAME}_{MODEL_NAME}_{GPU_NAME}.csv\"\n",
        "save_name_for_multi_run_compiled_results = f\"multi_run_compiled_results_{NUM_RUNS}_runs_{DATASET_NAME}_{MODEL_NAME}_{GPU_NAME}.csv\"\n",
        "\n",
        "# Create filepaths to save the results to\n",
        "multi_run_no_compile_save_path = f\"{pytorch_2_multi_run_results_dir}/{save_name_for_non_compiled_results}\"\n",
        "multi_run_compile_save_path = f\"{pytorch_2_multi_run_results_dir}/{save_name_for_compiled_results}\"\n",
        "print(f\"[INFO] Saving experiment 3 non-compiled results to: {multi_run_no_compile_save_path}\")\n",
        "print(f\"[INFO] Saving experiment 4 compiled results to: {multi_run_compile_save_path}\")\n",
        "\n",
        "# Save the results\n",
        "non_compile_results_multiple_runs_df.to_csv(multi_run_no_compile_save_path)\n",
        "compile_results_multiple_runs_df.to_csv(multi_run_compile_save_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVLrxEhnNUHC",
        "outputId": "0d83a32a-b124-4f61-89c3-96c6feed60d9"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving experiment 3 non-compiled results to: pytorch_2_results/multi_run_results/single_run_non_compiled_results_CIFAR10_ResNet50_NVIDIA_A100-SXM4-40GB.csv\n",
            "[INFO] Saving experiment 4 compiled results to: pytorch_2_results/multi_run_results/single_run_compiled_results_CIFAR10_ResNet50_NVIDIA_A100-SXM4-40GB.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Possible improvements and extensions \n",
        "\n",
        "1. More powerful CPUs - to load data to the GPU as quickly as possible, relative speedups come from using as much of the GPU as possible. \n",
        "2. Use mixed precision training - uses a combination of float32 and float16 to train a model (generally improves model training time).\n",
        "3. Transformer based models may see more *relative* speedups than convolutional - \n",
        "4. Train for longer - the longer we train, the more we can benefit from the optimization steps that `torch.compile()` does up front. \n",
        "\n",
        "See more here: https://www.learnpytorch.io/pytorch_2_intro/#5-possible-improvements-and-extensions\n",
        "\n",
        "## 6. Resources to learn more\n",
        "\n",
        "See here: https://www.learnpytorch.io/pytorch_2_intro/#6-resources-to-learn-more"
      ],
      "metadata": {
        "id": "acuIcGf_Naxf"
      }
    }
  ]
}